Unveiling the Hidden Feast: A Model to Translate Molecular Detection Into Predation Rate—Application Example on Biological Control by Generalist Predators in Agricultural Fields
Bibliographic record
Abstract
Few processes are as decisive as predation in shaping the structure and dynamics of ecological communities. For most predator species, however, the number of prey items killed by a predator in a day (predation rate) remains impossible to assess because direct observations are scarce or impossible to acquire. For such species, molecular gut content analyses are routinely used to test for the presence of a prey in the predator's gut. Specifically, our model uses a novel mechanistic representation of predation and digestion to integrate field data on prey detection and laboratory data on prey molecular signal decay in the predator's gut. Model fit provides an estimate of the slope and intercept of the digestion curve (molecular signal decay) and an estimate of the predation rate. In a case study targeting 25 carabid beetle species and 5 types of prey in agricultural fields (winter wheat), we use our model to estimate predation rates for each predator-prey pair. Based on predation rate estimates, we introduce a new biocontrol indicator at community scale and explore its potential for advanced agroecological research. We discuss the performance of our model on the basis of the scant information available in the literature and detail its conditions of application to highlight its advantages over existing predation models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".